"""simple docstring"""



import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       SqueezeBertForMaskedLM,
       SqueezeBertForMultipleChoice,
       SqueezeBertForQuestionAnswering,
       SqueezeBertForSequenceClassification,
       SqueezeBertForTokenClassification,
       SqueezeBertModel,
   )






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=True    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=5    ,		lowerCamelCase=4    ,		lowerCamelCase=64    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=512    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=0.02    ,		lowerCamelCase=3    ,		lowerCamelCase=4    ,		lowerCamelCase=None    ,		lowerCamelCase=2    ,		lowerCamelCase=2    ,		lowerCamelCase=2    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=1    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_input_mask
        __a       =    use_token_type_ids
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    type_vocab_size
        __a       =    type_sequence_label_size
        __a       =    initializer_range
        __a       =    num_labels
        __a       =    num_choices
        __a       =    scope
        __a       =    q_groups
        __a       =    k_groups
        __a       =    v_groups
        __a       =    post_attention_groups
        __a       =    intermediate_groups
        __a       =    output_groups


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    None
        if self.use_input_mask:
               __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

        __a       =    None
        __a       =    None
        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels						)
               __a       =    ids_tensor([self.batch_size]    ,		self.num_choices						)

        __a       =    self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 						a__   ( self						):
        return SqueezeBertConfig(
            embedding_size=self.hidden_size    ,		vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		attention_probs_dropout_prob=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		initializer_range=self.initializer_range    ,		q_groups=self.q_groups    ,		k_groups=self.k_groups    ,		v_groups=self.v_groups    ,		post_attention_groups=self.post_attention_groups    ,		intermediate_groups=self.intermediate_groups    ,		output_groups=self.output_groups    ,		)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    SqueezeBertModel(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		lowerCamelCase						)
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    SqueezeBertForMaskedLM(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    SqueezeBertForQuestionAnswering(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		start_positions=lowerCamelCase    ,		end_positions=lowerCamelCase						)
        self.parent.assertEqual(result.start_logits.shape    ,		(self.batch_size, self.seq_length)						)
        self.parent.assertEqual(result.end_logits.shape    ,		(self.batch_size, self.seq_length)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    SqueezeBertForSequenceClassification(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    SqueezeBertForTokenClassification(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()

        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_choices
        __a       =    SqueezeBertForMultipleChoice(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    input_ids.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    input_mask.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		labels=lowerCamelCase    ,		)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices)						)






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        ((__a) ,   (__a) ,   (__a) ,   (__a) ,   (__a) ,   (__a))       =    config_and_inputs
        __a       =    {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_torch
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[str]							  =						(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _snake_case       :					Optional[Any]							  =						(
     {
         """feature-extraction""": SqueezeBertModel,
         """fill-mask""": SqueezeBertForMaskedLM,
         """question-answering""": SqueezeBertForQuestionAnswering,
         """text-classification""": SqueezeBertForSequenceClassification,
         """token-classification""": SqueezeBertForTokenClassification,
         """zero-shot""": SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _snake_case       :					Tuple							  =						False
 _snake_case       :					str							  =						True
 _snake_case       :					Optional[int]							  =						False


 def 						a__   ( self						):
        __a       =    SqueezeBertModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase    ,		dim=37						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase						)






 @slow
 def 						a__   ( self						):
        for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =    SqueezeBertModel.from_pretrained(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)






@require_sentencepiece
@require_tokenizers
@require_torch
class  snake_case__      (       unittest.TestCase       ):


 @slow
 def 						a__   ( self						):
        __a       =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"						)

        __a       =    torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]]						)
        __a       =    model(lowerCamelCase						)[0]
        __a       =    torch.Size((1, 3)						)
        self.assertEqual(output.shape    ,		lowerCamelCase						)
        __a       =    torch.tensor([[0.6401, -0.0349, -0.6041]]						)
        self.assertTrue(torch.allclose(lowerCamelCase    ,		lowerCamelCase    ,		atol=1E-4						)						)

"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
